Basic Workflow Tracing

Enable tracing and observability for workflows.

This example shows how to enable tracing for a workflow. When tracing is enabled, all workflow operations, step runs, model calls, and tool executions are automatically captured and stored in your database.

1. Create a Python file

   basic_workflow_tracing.py

   from agno.agent import Agent
   from agno.db.sqlite import SqliteDb
   from agno.os import AgentOS
   from agno.tools.hackernews import HackerNewsTools
   from agno.workflow.condition import Condition
   from agno.workflow.step import Step
   from agno.workflow.types import StepInput
   from agno.workflow.workflow import Workflow
   
   # Set up database
   db = SqliteDb(db_file="tmp/traces.db")
   
   # === BASIC AGENTS ===
   researcher = Agent(
   name="Researcher",
   instructions="Research the given topic and provide detailed findings.",
   tools=[HackerNewsTools()],
   )
   
   summarizer = Agent(
   name="Summarizer",
   instructions="Create a clear summary of the research findings.",
   )
   
   fact_checker = Agent(
   name="Fact Checker",
   instructions="Verify facts and check for accuracy in the research.",
   tools=[HackerNewsTools()],
   )
   
   writer = Agent(
   name="Writer",
   instructions="Write a comprehensive article based on all available research and verification.",
   )
   
   # === CONDITION EVALUATOR ===
   
   
   def needs_fact_checking(step_input: StepInput) -> bool:
   """Determine if the research contains claims that need fact-checking"""
   return True
   
   
   # === WORKFLOW STEPS ===
   research_step = Step(
   name="research",
   description="Research the topic",
   agent=researcher,
   )
   
   summarize_step = Step(
   name="summarize",
   description="Summarize research findings",
   agent=summarizer,
   )
   
   # Conditional fact-checking step
   fact_check_step = Step(
   name="fact_check",
   description="Verify facts and claims",
   agent=fact_checker,
   )
   
   write_article = Step(
   name="write_article",
   description="Write final article",
   agent=writer,
   )
   
   # === BASIC LINEAR WORKFLOW ===
   workflow = Workflow(
   name="Basic Linear Workflow",
   description="Research -> Summarize -> Condition(Fact Check) -> Write Article",
   db=db,
   steps=[
       research_step,
       summarize_step,
       Condition(
           name="fact_check_condition",
           description="Check if fact-checking is needed",
           evaluator=needs_fact_checking,
           steps=[fact_check_step],
       ),
       write_article,
   ],
   )
   
   # Run the workflow - traces are captured automatically
   workflow.print_response("Write an article on AI agents?")
   
   # Query traces from the database
   traces, count = db.get_traces(workflow_id=workflow.id, limit=10)
   print(f"\nFound {count} traces for workflow '{workflow.name}'")
   for trace in traces:
       print(f"  - {workflow.name}: {trace.duration_ms}ms ({trace.status})")
   
2. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U openai agno opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno
   
4. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. Run the workflow

   Mac

   python basic_workflow_tracing.py
   

   python basic_workflow_tracing.py